def indexToID(pop, idField='ind_id', fatherField='father_id', motherField='mother_id',
fatherIndex='father_idx', motherIndex='mother_idx', reset=False):
'''This function adds information field idField (default to ``'ind_id'``)
to population ``pop`` and assigns an unique ID to every individual in this
Population. It then adds information fields fatherField (default to
``'fatherField'``) and motherField (default to ``'motherField'``) and set
their values with IDs according to the established index based
parents-children relationship. Existing information fields will be used if
idField, fatherField or motherField already exist. This function uses a
system-wide ID generator for unique IDs, which does not have to start from
zero. A parameter ``reset`` could be used to reset starting ID to zero
(if ``reset=True``) or a specified number (``reset=number``).
'''
pop.addInfoFields([idField, fatherField, motherField], -1)
# set each individual's unique ID to idField
tagID(pop, reset=reset, infoFields=idField)
# save each individual's parents' IDs to fatherField and motherField
for gen in range(pop.ancestralGens()-1, -1, -1):
pop.useAncestralGen(gen)
for ind in pop.individuals():
if ind.info(fatherIndex) != -1:
father = pop.ancestor(ind.info(fatherIndex), gen+1)
ind.setInfo(father.info(idField), fatherField)
if ind.info(motherIndex) != -1:
mother = pop.ancestor(ind.info(motherIndex), gen+1)
ind.setInfo(mother.info(idField), motherField)
def initialize_tuson_pop(self, chromosome_lengths, info_fields,
all_genotypes_handle):
tuson_founders = sim.Population(size=105, loci=chromosome_lengths,
infoFields=info_fields)
for ind, genotype in zip(tuson_founders.individuals(),
all_genotypes_handle):
ind.setGenotype(genotype)
sim.tagID(tuson_founders, reset=True)
return tuson_founders
def drawSample(self, pop, penet, nFamilies):
self.pop = pop.clone()
self.pop.addInfoFields(['ind_id', 'father_id', 'mother_id'])
self.pop.setAncestralDepth(1)
sim.tagID(self.pop, reset=True)
self.pop.evolve(
preOps = penet,
matingScheme=sim.RandomMating(ops=[
sim.MendelianGenoTransmitter(), # pass genotype
sim.IdTagger(), # assign new ID to offspring
sim.PedigreeTagger(), # record the parent of each offspring
penet, # determine offspring affection status
sim.DiscardIf(cond=self._discardTrio)
], subPopSize=nFamilies),
gen = 1
)
return self.pop
recombination_rates = []
for chromosome in cM_positions:
for cM in chromosome:
if str(cM)[-2:] == '.6':
recombination_rates.append(0.01)
else:
recombination_rates.append(0.0)
flat_cM_positions = []
for cMs in cM_positions:
flat_cM_positions.extend(cMs)
# In[ ]:
nam = sim.loadPopulation('nam_prefounders.pop')
sim.tagID(nam, reset=True)
nam.setSubPopName('prefounders', 0)
sample_sizes = {i: 100 for i in range(0, 21, 2)}
locus_names = list(range(nam.totNumLoci()))
genetic_structure = {}
#genetic_structure['cM_positions'] = cM_positions
#enetic_structure['chr_cM_positions'] = chr_cM_positions
genetic_structure['allele_names'] = allele_names
genetic_structure['integral_valued_loci'] = integral_valued_loci
genetic_structure[
'relative_integral_valued_loci'] = relative_integral_valued_loci
genetic_structure['alleles'] = alleles
genetic_structure['recombination_rates'] = recombination_rates
# In[ ]:
generations = 10
replicates = 100
td = TusonDrift(genetic_map_filename=map_filename, population_size=popsize,
number_of_breeding_females=females,
number_of_breeding_males=males,
number_of_generations=generations,
founder_population_filename=founder_filename,
population_structure_filename=popst_filename)
recom_rates = parser.parse_recombination_rates(map_filename)
tuson = sim.loadPopulation(td.founder_population_filename)
tuson.setSubPopName('tuson', 0)
sim.tagID(tuson, reset=True)
# tuson_meta = td.initialize_meta_population(tuson, number_of_reps=replicates)
# sites, inds = td.find_fixed_sites(tuson, 0.15, 0.03)
sim.stat(tuson, numOfSegSites=sim.ALL_AVAIL,
vars=['numOfFixedSites', 'fixedSites'])
sites = tuson.dvars().fixedSites
# Assigns popoulation structure
ps = parameterizer.PopulationStructure(tuson, td.population_structure_filename,
0.15, 0.03)
input_file_prefix = '/home/vakanas/BISB/rjwlab-scripts/saegus_project/devel/magic/1478/'
mg = analyze.MultiGeneration('epsilon')
run_id = 'epsilon'
generations = 10
heritability = 0.7
number_of_qtl = 50
number_of_replicates = 2
founders = [[2, 26], [3, 25], [4, 24], [5, 23]]
os_per_pair = 500
recombination_rates = [0.01] * 1478
prefounders = sim.loadPopulation(input_file_prefix + 'bia_prefounders.pop')
config_file_template = input_file_prefix + 'gwas_pipeline.xml'
sim.tagID(prefounders, reset=True)
alleles = np.array(
pd.read_hdf(input_file_prefix + 'parameters/alleles_at_1478_loci.hdf'))
rdm_populations = sim.Simulator(prefounders,
number_of_replicates,
stealPops=False)
rdm_magic = breed.MAGIC(rdm_populations, founders, recombination_rates)
sim.tagID(prefounders, reset=27)
rdm_magic.generate_f_one(founders, os_per_pair)
sim.stat(rdm_populations.population(0), alleleFreq=sim.ALL_AVAIL)
af = analyze.allele_data(rdm_populations.population(0), alleles,
list(range(len(alleles))))
minor_alleles = np.asarray(af.minor_allele, dtype=np.int8)
rdm_magic.recombinatorial_convergence(rdm_populations, len(founders),
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This script is an example in the simuPOP user's guide. Please refer to
# the user's guide (http://simupop.sourceforge.net/manual) for a detailed
# description of this example.
#
import simuPOP as sim
pop = sim.Population(10, infoFields='ind_id', ancGen=1)
pop.evolve(initOps=sim.IdTagger(),
matingScheme=sim.RandomSelection(ops=[
sim.CloneGenoTransmitter(),
sim.IdTagger(),
]),
gen=1)
print([int(ind.ind_id) for ind in pop.individuals()])
pop.useAncestralGen(1)
print([int(ind.ind_id) for ind in pop.individuals()])
sim.tagID(pop) # re-assign ID
print([int(ind.ind_id) for ind in pop.individuals()])
ploidy=2,
loci=chromosome_lengths,
lociNames=founder.snpID.astype(str))
#Set genotypes in simuPOP populations from converted genotypes
for i, ind in enumerate(example_pop.individuals()):
ind.setGenotype(converted_genotypes[i])
#check genotype for Parent A (index 0), can repeat for all founders
example_individual = example_pop.individual(0)
example_genotype = np.array(example_individual.genotype(ploidy=0, chroms=0))
print(example_genotype)
#add required info fields
example_pop.addInfoFields(['ind_id', 'mother_id', 'father_id'])
sim.tagID(
example_pop, reset=1
) #assigns unique id to all founders starting from 1, will be used for tracking allele origin
#Create list of crossover probabilities from founder data measured in centimorgans
#parse.RecomRates() is designed to be used to read in genetic map and then parse, not currently
#set to parse recombination rates from data already read in (genetic_map above)
tf = parse.RecomRates()
recom_map = tf.parse_recombination_rates('founder_key_25K_vcf_28FEB20.txt')
### Generate F1 data
### Set founders and designate number of offspring for F1
founders = [[1, 2], [3, 4], [5, 6], [7, 3]]
offspring_per_pair = 1 #One offspring per pair, 4 individuals total
founder_chooser = breed.PairwiseIDChooser(founders, offspring_per_pair)
number_of_pairs = len(founders)
def test_generate_operating_population():
genetic_map = pd.read_csv('nam_prefounders_genetic_map.txt', index_col=None,
sep='\t')
pf_map = shelve.open('pf_map')
misc_gmap = shelve.open('misc_gmap')
uniparams = shelve.open('uniparams')
locus_names = uniparams['locus_names']
pos_column = uniparams['pos_column']
allele_names = uniparams['allele_names']
snp_to_integer = uniparams['snp_to_integer']
integer_to_snp = uniparams['integer_to_snp']
alleles = misc_gmap['alleles']
chr_cM_positions = misc_gmap['chr_cM_positions']
cM_positions = misc_gmap['cM_positions']
integral_valued_loci = misc_gmap['integral_valued_loci']
relative_integral_valued_loci = misc_gmap['relative_integral_valued_loci']
recombination_rates = misc_gmap['recombination_rates']
nam = sim.loadPopulation(uniparams['prefounder_file_name'])
sim.tagID(nam, reset=True)
nam.setSubPopName('maize_nam_prefounders', 0)
selection_statistics = {
'aggregate': {},
'selected': {},
'non-selected': {}
}
ind_names_for_gwas = {i: {} for i in range(uniparams[
'number_of_replicates'])}
uniparams['meta_pop_sample_sizes'] = {i: 100 for i in
range(0, uniparams['generations_of_selection'] + 1, 2)
}
s = simulate.Truncation(uniparams['generations_of_selection'],
uniparams['generations_of_random_mating'],
uniparams['operating_population_size'],
uniparams[
'proportion_of_individuals_saved'],
uniparams['overshoot_as_proportion'],
uniparams['individuals_per_breeding_subpop'],
uniparams['heritability'],
uniparams['meta_pop_sample_sizes'],
uniparams['number_of_replicates'])
ind_names_for_gwas = {i: {} for i in range(uniparams[
'number_of_replicates'])}
founders = uniparams['founders']
replicated_nam = sim.Simulator(nam, rep=2, stealPops=False)
pop = replicated_nam.extract(0)
assert pop.popSize() == 26, "Population is too large."
s.generate_f_one(pop, recombination_rates, founders, 100)
assert pop.popSize() == 400, "Population should have size: {} after the F_1 mating " \
"procedure." \
"".format(len(founders) * 100)
#pop.splitSubPop(0, [100] * 4)
#subpop_list = list(range(pop.numSubPop()))
intmd_os_struct = s.restructure_offspring(pop, 100, 4)
snd_order = breed.SecondOrderPairIDChooser(intmd_os_struct, 1)
pop.evolve(
preOps=[sim.MergeSubPops()],
matingScheme=sim.HomoMating(
sim.PyParentsChooser(snd_order.snd_ord_id_pairs),
sim.OffspringGenerator(ops=[
sim.IdTagger(),
sim.ParentsTagger(),
sim.PedigreeTagger(),
sim.Recombinator(rates=recombination_rates)
],
numOffspring=1),
subPopSize=[200],
),
gen=1,
)
assert pop.popSize() == 1, "Population does not have correct size after second round of mating."
second_intmd_os_struct = s.restructure_offspring(pop, 100, 2)
third_order = breed.SecondOrderPairIDChooser(second_intmd_os_struct, 1)
pop.evolve(
preOps=[sim.MergeSubPops()],
matingScheme=sim.HomoMating(
sim.PyParentsChooser(third_order.snd_ord_id_pairs),
sim.OffspringGenerator(ops=[
sim.IdTagger(),
sim.ParentsTagger(),
sim.PedigreeTagger(),
sim.Recombinator(rates=recombination_rates)
],
numOffspring=1),
subPopSize=[100],
),
gen=1,
)
assert pop.popSize() == 100, "Second merge of breeding sub-populations. Offspring population does not have " \
"correct size"
N=(8100, 8100, 7900, 900000),
G=(20000, 10, 370),
mu=1.8e-8,
steps=[100, 1, 10],
selModel='multiplicative',
selDist='constant',
selCoef=None,
popFile='example.pop')
# load population
# print('Loading population example.pop')
# pop = sim.loadPopulation('example.pop')
# evolve the population for a few more generations to produce pedigrees
print('Evolving the population for three generations.')
pop.addInfoFields(['ind_id', 'father_id', 'mother_id'])
sim.tagID(pop)
# save all ancestral generations during evolution
pop.setAncestralDepth(-1)
pop.evolve(matingScheme=sim.RandomMating(
numOffspring=(sim.UNIFORM_DISTRIBUTION, 2, 4),
ops=(sim.MendelianGenoTransmitter(), sim.IdTagger(),
sim.PedigreeTagger())),
gen=3)
# what is the average number of mutants in this population?
avgMutants = (pop.popSize() * pop.totNumLoci() * 2. -
pop.genotype().count(0)) / pop.popSize()
print(('Average number of mutants is %.2f' % avgMutants))
#
# This contains marker information for the initial population
print('Mutant locations are saved to file sample.map')
markers = saveMarkerInfoToFile(pop, 'pedigree.map')
def do_forward_sims(sim_data, chrom_len, diploid_Ne, batchname, repilcates,
simupop_seed):
print("start getting chromosomes positions")
chromosome_positions = get_chromosome_positions(sim_data=sim_data,
chromsome_length=chrom_len)
print("done getting chromosomes positions")
haplotypes = get_haplotypes(sim_data)
loci_per_chromsome = get_loci_per_chromosome(chromosome_positions)
n_chrom = len(loci_per_chromsome)
# set up the ancestral pop in simuPOP
initial = simuPOP.Population(
diploid_Ne, # here is the diploid number
loci=
loci_per_chromsome, # should be the number of loci on each chromosome
lociPos=list(chromosome_positions),
ploidy=2,
infoFields=['father_idx', 'mother_idx', 'ind_id'],
#alleleNames=['A', 'C', 'G', 'T'],
lociNames=[
'locus_{}'.format(x) for x in xrange(len(chromosome_positions))
])
simuPOP.initGenotype(initial,
prop=[1.0 / len(haplotypes)] * len(haplotypes),
haplotypes=list(haplotypes))
simuPOP.tagID(initial, reset=1)
initial_export = get_export_genotypes(initial, initial.popSize())
np.savetxt('./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}.inital.txt'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom),
initial_export,
delimiter='\t',
fmt='%01d')
# map-ped
simuPOP.utils.export(
initial,
format='PED',
output='./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}.inital.ped'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom),
gui=False,
idField='ind_id')
simuPOP.utils.export(
initial,
format='MAP',
output='./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}.inital.map'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom),
gui=False)
# Doesn't yet work!!
# set the seed for simuPOP
#simuPOP.setRNG(seed=simupop_seed)
# and in Python
#random.seed(simupop_seed)
# and for numpy
#np.random.seed(simupop_seed)
print("initalizing the forward simulator")
simu = simuPOP.Simulator(
simuPOP.Population(
diploid_Ne, # here is the diploid number
loci=get_loci_per_chromosome(
chromosome_positions
), # should be the number of loci on each chromosome
lociPos=list(chromosome_positions),
ploidy=2,
infoFields=['ind_id', 'father_idx', 'mother_idx'],
#alleleNames=['A', 'C', 'G', 'T'],
lociNames=[
'locus_{}'.format(x) for x in xrange(len(chromosome_positions))
]),
rep=repilcates)
print("Start evolving {} replicates".format(repilcates))
simu.evolve(
initOps=[
simuPOP.InitSex(
sex=[simuPOP.MALE, simuPOP.FEMALE]), # alternate sex
simuPOP.InitGenotype(prop=[1.0 / len(haplotypes)] *
len(haplotypes),
haplotypes=list(haplotypes))
],
matingScheme=simuPOP.HomoMating(
chooser=simuPOP.PyParentsChooser(fixedChooser),
generator=simuPOP.OffspringGenerator(
sexMode=(simuPOP.GLOBAL_SEQUENCE_OF_SEX, simuPOP.MALE,
simuPOP.FEMALE),
ops=[
simuPOP.Recombinator(intensity=1.0 / chrom_len),
simuPOP.ParentsTagger()
]),
),
postOps=[],
gen=20)
print("Done evolving {} replicates!".format(repilcates))
# export the data
print("Exporting data!".format(repilcates))
for rep, pop in enumerate(simu.populations()):
if diploid_Ne >= 200:
pop_genotypes = get_export_genotypes(pop,
n_ind=200) # select 200 inds
else:
pop_genotypes = get_export_genotypes(
pop, n_ind=diploid_Ne) # select 200 inds
np.savetxt('./share/{}/Ne-{}_Chr-{}/Ne-{}_Chr-{}_Frep-{}.geno'.format(
batchname, diploid_Ne, n_chrom, diploid_Ne, n_chrom,
rep).format(rep),
pop_genotypes,
delimiter='\t',
fmt='%01d')
if rep % 10 == 0:
print "saved rep {}".format(rep)
#!/home/vakanas/anaconda43/python3.6
import simuOpt
simuOpt.setOptions(alleleType='short', quiet=True, numThreads=4)
import simuPOP as sim
import numpy as np
import pandas as pd
import random
import h5py
from saegus import analyze, operators, parameters
#np.set_printoptions(suppress=True, precision=5)
example_pop = sim.loadPopulation('example_pop.pop')
example_pop.addInfoFields(['ind_id', 'mother_id', 'father_id', 'g', 'p'])
sim.tagID(example_pop)
sim.stat(example_pop,
numOfSegSites=sim.ALL_AVAIL,
vars=['numOfSegSites', 'segSites', 'fixedSites'])
sim.stat(example_pop, alleleFreq=sim.ALL_AVAIL)
segregating_loci = example_pop.dvars().segSites
allele_states = analyze.gather_allele_data(example_pop)
allele_frequencies = analyze.gather_allele_frequencies(example_pop,
allele_states)
gwas = analyze.GWAS(example_pop, np.array(segregating_loci, dtype=np.int_),
allele_states[:, 3], 'example')
count_matrix = gwas.calculate_count_matrix('example_count_matrix.txt')
gwas.hapmap_formatter(hapmap_file_name='example_hapmap.txt')
eigenvalues, eigenvectors = gwas.pop_struct_eigendecomp(count_matrix)
gwas.population_structure_formatter(
eigenvalues,
eigenvectors,